"One of the best programmers I have dealt with, a very organized person, committed to deadlines and professional work
I worked with him on a news classification model and he gave me very wonderful and satisfactory results
After the work was completed, he contacted me and explained the mechanism of the program in detail and explained the code line by line
I am really very happy to work with him and for sure I will work with him more than once"
- Mohammed A.
My name is Zied, and I’d like to thank you for taking the time to review my profile.
I am a Mathematician and Python Developer with an Engineering Degree and over 6 years of comprehensive professional experience. I specialize in a number of different niche areas of development, including (but not limited to):
Natural Language Processing
Scientific Book Writing
Beyond that, I also write my scientific articles in Latex and can create and review scientific content in proper, grammatical English.
If you’re in need of any of these services or are simply interested in working with me and my skill set, do not hesitate to let me know! I’m always interested in working with passionate clients and interesting projects.
Zied did a very good job in a constrained time. He doesn't communicate much but the work is done and that's what counts.
6 months ago
Apr 2015 - Jul 2019 (4 years, 3 months)
Explore new methods for information extraction .
Developing tools for web scraping.
Université René Descartes (Paris V), France 2017 - 2018
Tweet Sentiment Extraction using Viterbi Algorithm with Transfer Learning
The process of tweet sentiment extraction involves identifying the most impactful part of a sentence and determining whether the sentiment expressed is positive or negative. The objective of this research is to pinpoint the specific part of tweet sentences that evoke emotions.
External knowledge transfer deployment inside a simple double agent Viterbi algorithm
We consider in this paper deploying external knowledge transfer inside a simple double agent Viterbi algorithm which is an algorithm firstly introduced by the author in his preprint "Hidden Markov Based Mathematical Model dedicated to Extract Ingredients from Recipe Text". The key challenge of this work lies in discovering the reason why our old model does have bad performances when it is confronted with estimating ingredient state for unknown words .
Hidden Markov Based Mathematical Model dedicated to Extract Ingredients from Recipe Text
On this work, I performed a mathematical model based on Hidden Markov structures and I obtained a high level accuracy of ingredients extracted from text recipe which is a performance greater than what traditional methods could make without unknown words consideration.